## New names:
## • `EARNMORE` -> `EARNMORE...29`
## • `EARNMORE` -> `EARNMORE...31`

The first section explores how views differ with age and sex- we have 4 boxplots exploring different variables.

## New names:
## • `EARNMORE` -> `EARNMORE...29`
## • `EARNMORE` -> `EARNMORE...31`

The section below explores how views differ with political party affiliation.

Aside from looking at the distribution of questions over gender and sex, how might where someone lives (or at least where the GSS interview was conducted for the participant) affect their viewpoints?

The GSS data is organized not by state, but by Census-assigned regions.

## Reading layer `States_shapefile' from data source 
##   `C:\Priyansha\Columbia University\Data visualization\Final project working\States_shapefile.shp' 
##   using driver `ESRI Shapefile'
## Simple feature collection with 51 features and 6 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -178.2176 ymin: 18.92179 xmax: -66.96927 ymax: 71.40624
## Geodetic CRS:  WGS 84
## `summarise()` has grouped output by 'REGION', 'FAIRHWRK'. You can override
## using the `.groups` argument.

Looking at the percentage of those who answered that the division of labor between the respondent and their spouse being fair for both, we can see those who believe so on the higher end (22-24%) are located in the New England area and the Mountain area, compared to the West North Central area (16%). Further adding context about the spouse-respondent dynamics and lifestyle should be further analyzed to understand if the feelings of fairness might stem from a breadwinner vs. caretaker role, along with potential political party perspectives on more traditional gender/family norms.